2 + 2
## [1] 4
nhanes_small
## # A tibble: 10,000 x 14
## age sex height weight bmi diabetes diabetes_age phys_active_days
## <int> <fct> <dbl> <dbl> <dbl> <fct> <int> <int>
## 1 34 male 165. 87.4 32.2 No NA NA
## 2 34 male 165. 87.4 32.2 No NA NA
## 3 34 male 165. 87.4 32.2 No NA NA
## 4 4 male 105. 17 15.3 No NA NA
## 5 49 female 168. 86.7 30.6 No NA NA
## 6 9 male 133. 29.8 16.8 No NA NA
## 7 8 male 131. 35.2 20.6 No NA NA
## 8 45 female 167. 75.7 27.2 No NA 5
## 9 45 female 167. 75.7 27.2 No NA 5
## 10 45 female 167. 75.7 27.2 No NA 5
## # ... with 9,990 more rows, and 6 more variables: phys_active <fct>,
## # tot_chol <dbl>, bp_sys_ave <int>, bp_dia_ave <int>, smoke_now <fct>,
## # poverty <dbl>
nhanes_small %>%
filter(!is.na(diabetes)) %>%
group_by(diabetes, sex) %>%
summarise(mean_age = mean(age, na.rm = TRUE),
mean_bmi = mean(bmi, na.rm = TRUE)) %>%
ungroup() %>%
knitr::kable(caption = "Table 1. Mean Age and BMI.")
## `summarise()` has grouped output by 'diabetes'. You can override using the `.groups` argument.
| diabetes | sex | mean_age | mean_bmi |
|---|---|---|---|
| No | female | 36.46581 | 26.21885 |
| No | male | 34.34953 | 26.10141 |
| Yes | female | 59.90476 | 33.70212 |
| Yes | male | 58.64764 | 31.53878 |
Image by Dimitri Houtteman from Pixabay.
knitr::include_graphics(here::here("c:/Users/au322271/Desktop/LearningR/doc/images/kitten.jpg"))
Kitten attacking flowers!